Transient Signals Research Articles

Ultrafast dynamics in plasmon-exciton core-shell systems: the role of heat.

Strong coupling between plasmons and excitons gives rise to new hybrid polariton states with potential applications in various fields. Despite a plethora of research on plasmon-exciton systems, their transient behaviour is not yet fully understood. Besides Rabi oscillations in the first few femtoseconds after optical excitation, coupled systems show interesting non-linear features on the picosecond time scale. Here, we conclusively show that the source of these features is heat that is generated inside the particles. Until now, this hypothesis was only based on phenomenological arguments. We investigate the role of heat by recording the transient spectra of plasmon-exciton core-shell nanoparticles with excitation off the polariton resonance. We present analytical simulations that precisely recreate the measurements solely by assuming an initial temperature rise of the electron gas inside the particles. The simulations combine established strategies for describing uncoupled plasmonic particles with a recently published model for static spectra. The simulations are consistent for various excitation powers, confirming that heating of the particles is indeed the root of the changes in the transient signals.

Model and experimental research of the transient process for the improvement of the traveling wave fault location

The results of modeling of transient signals in a single-wire line are considered. The parameters of voltage and current signals are compared. Signal oscillation’s amplitude and period are given attention. The algorithm for determining the transient signal’s onset time (TSOT) under the conditions of noise and oscillations of the signal’s leading edge is presented. The results of experimentally recorded signals’ analyses are given.

Diversity of dynamic voltage patterns in neuronal dendrites revealed by nanopipette electrophysiology.

Dendrites and dendritic spines are the essential cellular compartments in neuronal communication, conveying information through transient voltage signals. Our understanding of these compartmentalized voltage dynamics in fine, distal neuronal dendrites remains poor due to the difficulties inherent to accessing and stably recording from such small, nanoscale cellular compartments for a sustained time. To overcome these challenges, we use nanopipettes that permit long and stable recordings directly from fine neuronal dendrites. We reveal a diversity of voltage dynamics present locally in dendrites, such as spontaneous voltage transients, bursting events and oscillating periods of silence and firing activity, all of which we characterized using segmentation analysis. Remarkably, we find that neuronal dendrites can display spontaneous hyperpolarisation events, and sustain transient hyperpolarised states. The voltage patterns were activity-dependent, with a stronger dependency on synaptic activity than on action potentials. Long-time recordings of fine dendritic protrusions show complex voltage dynamics that may represent a previously unexplored contribution to dendritic computations.

Fault transient feature extraction method of 66kV transmission line based on improved wavelet threshold and CEEMDAN-FME

In the 66kV transmission line, the compensation effect of the arc suppression coil, so the amplitude of fault zero sequence current is weak after the occurrence of single-phase grounding responsibility. The fault characteristics are not obvious, and there is environmental noise interference during fault signal acquisition, which further increases the difficulty of ground fault feature extraction. To solve the above problems, a fault transient feature extraction method of 66kV transmission line is proposed, based on improved wavelet threshold and Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) - Fuzzy measure entropy (FME). The characteristics of single-phase grounding transient fault in small current grounding system are analyzed. The transient fault signal was preprocessed by an improved wavelet threshold method, and the transient fault characteristics were extracted accurately by CEEMDAN-FME processing. The proposed method has good anti-noise performance through the simulation comparison and verification of denoising processing and transient feature extraction by MATLAB. It can effectively extract fault features when the interference degree is significant.

S-18-2: HYPERTENSION AS A DRIVER OF CEREBRAL SMALL VESSEL DISEASE AND DEMENTIA

Hypertension is the principal cause of vascular dementia. Unfortunately however, lowering blood pressure after the onset of cognitive decline does not slow disease progression and currently, vascular dementia is a disease with no available treatment. A principal feature of hypertension-related vascular dementia is a reduction in cerebral blood flow. Precisely how elevated blood pressure reduces cerebral blood flow is unknown and to investigate this further we used a polygenic model of hypertension, the BPH/2 mouse. Aged 8 months, BPH/2 mice exhibited cognitive impairment mimicking the human presentation of vascular dementia and in addition, cerebral blood flow was reduced. Small cerebral resistance arteries which run across the surface of the brain (pial arteries) showed enhanced pressure-induced constriction due to lack of activity of large conductance Ca2+ activated K+ channels (BK channels), key vasodilatory ion channels of cerebral vascular smooth muscle cells. BK channels are activated by transient intracellular signals from the sarcoplasmic reticulum called Ca2+ sparks, leading to hyperpolarisation and vasodilation. Combining patch clamp electrophyiology, high speed confocal imaging and proximity ligation assays we demonstrated that while the biophysical properties of BK channels and Ca2+ sparks remained intact, physical separation of the plasma membrane from the sarcoplasmic reticulum uncoupled the vasodilatory mechanism. This novel pathogenic mechanism is responsible for the increased constriction and can be now targeted as a possible avenue to restore healthy cerebral blood flow in vascular dementia.

Photonics-Assisted Sensing of Non-Repetitive Transient Weak Signal with Sensitivity Enhancement

Photonics-Assisted Sensing of Non-Repetitive Transient Weak Signal with Sensitivity Enhancement

On the impact of compact binary merger ejecta opacity on Kilonova transient signals

Returns of gravitational wave astronomy will largely benefit from the detection and identification of electromagnetic (EM) signatures to gravitationalwave sources. Kilonovae (KNe) are promising EM counterparts to compact binary mergers, offering to astronomers and nuclear astrophysicists a unique window to advance knowledge on the heavy-element nucleosynthesis and mergerdriven mass ejection. However, extremely heterogeneous post-merging ejecta composition of both light- and heavy-r process nuclei, implies strong effects on the KNe light-curve identification due to the varying opacity of the system. Hence, large uncertainties on the r-process final abundance via spectroscopic analysis of KNe signals are still present, hardly fixed by theoretical models. Here we will present some peculiar features of KNe studies, focusing on the opacity issue, from the atomic and plasma physics perspectives. In this view, efforts have been made recently at INFN-LNS, trying to put constraints on plasma opacity of interest for early-stage KNe emission. We will present the experimental progress on the problem, including instruments and methods which could open an interdisciplinary approach to tackle astrophysical problems in laboratory plasmas.

Improving Detection of Unsteady Crustal Deformation by using GNSS Data: Common Mode Error Reduction, Target Area Expansion to Hyuga-Nada, and Transient Signal Monitoring with Postseismic Deformation Removal

Improving Detection of Unsteady Crustal Deformation by using GNSS Data: Common Mode Error Reduction, Target Area Expansion to Hyuga-Nada, and Transient Signal Monitoring with Postseismic Deformation Removal

State-selective probing of CO2 autoionizing inner valence Rydberg states with attosecond extreme ultraviolet four-wave-mixing spectroscopy

Nonlinear spectroscopies can disentangle spectra that are congested due to inhomogeneous broadening. In conjunction with theoretical calculations, attosecond extreme ultraviolet (XUV) four-wave-mixing (FWM) spectroscopy is utilized here to probe the dynamics of autoionizing inner valence excited Rydberg states of the polyatomic molecule, ${\mathrm{CO}}_{2}$. This tabletop nonlinear technique employs a short attosecond XUV pulse train and two noncollinear, few-cycle near-infrared pulses to generate background-free XUV wave-mixing signals. FWM emission is observed from the $n=5--7$ states of the Henning sharp $nd{\ensuremath{\sigma}}_{g}$ Rydberg series that converges to the ionic $\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{B}\phantom{\rule{4pt}{0ex}}^{2}\mathrm{\ensuremath{\Sigma}}_{u}^{+}$ state. However, these transient emission signals decay with lifetimes of 33 $\ifmmode\pm\else\textpm\fi{}$ 6, 53 $\ifmmode\pm\else\textpm\fi{}$ 2, and 94 $\ifmmode\pm\else\textpm\fi{}$ 2 fs, respectively, which calculations show are consistent with the lifetimes of the short-lived $n=6--8$ members of the $ns{\ensuremath{\sigma}}_{g}$ character Henning diffuse Rydberg series. The oscillator strengths of transitions between states involved in all possible resonant FWM processes are calculated, verifying that the nonlinear spectra are dominated by pathways described by an initial excitation to the diffuse $ns{\ensuremath{\sigma}}_{g}$ Rydberg series and emission from the sharp $nd{\ensuremath{\sigma}}_{g}$ Rydberg series. The results substantiate not only that attosecond XUV FWM spectroscopy produces rigorous and meaningful measurements of ultrafast dynamics in polyatomic systems, but also that nonlinear spectroscopic techniques are versatile tools to selectively probe dynamics that are otherwise difficult to access.

Transient flow analysis for pumping system comprising pressure vessel using unsteady friction model

Recent myriad studies on the application of pressure vessels (PVs) to dampen the potentially hazardous transient pressure caused by pump failure are available in the literature. However, little research has been done on integrating these PVs into an instantaneous acceleration-based (IAB) model for transient flow analysis, while most of the reported investigations have been accomplished using the steady-state friction models. Also, scarce literature exists on optimizing the connection configuration of the PV. Nevertheless, unsteady friction (UF) is vital in accurately modeling a transient event, and therefore this study utilizes the IAB UF model to accurately predict the transient response of the pumping pipeline protected by a PV. Based on the proposed numerical model and computational algorithms, a novel approach is also explored to increase the working efficiency of the PV during transient events via a modified connection configuration with the main pipeline. A parametric study has been conducted to determine the impact of the PV's numerous parameters on transient pressure signals. The results reveal that the IAB UF model with two decay parameters can accurately replicate the amplitude and form of transient waves in a pumping system comprising a PV, with almost no deviations between numerical and experimental results. Furthermore, the numerical study shows that the initial air volume, polytropic exponent, installation location, PV size, and connection arrangement of PV with the pump-rising pipeline significantly affect the performance of water hammer protection. However, the flow resistance of connecting pipes containing perforated plates in a separate bypass line reduces the desired vessel volume from the standpoint of economy. The energy-based approach corroborates the use of this design for vessel-to-pipeline connections. The redesigned connection configuration reduces the required volume of PV by up to 70%, which leads to cost-effectiveness.

An Analytical Approach to Calculate Soft Error Rate Induced by Atmospheric Neutrons

In the atmosphere, it is generally understood that neutrons are the main contributor to the soft error rate (SER) in electronic devices. These particles are indeed able to trigger nuclear reactions in the sensitive regions of the devices, leading to secondary ions that may ionize the matter sufficiently to upset a memory cell or induce a transient signal, known as soft errors. For reliability purposes, it is crucial to be able to estimate the SER associated with a given technology, which is typically characterized by its sensitive volume and its threshold linear energy transfer (LET). As an alternative to the usual Monte Carlo methods, in this work we present an analytical model for SER prediction, where we separate the radiation–matter interaction from the geometry considerations (sensitive volume). By doing so, we show that the SER can be expressed as the sum of two contributions that can be calculated for any threshold LET and any sensitive volume size. We compare our proposed approach to existing Monte Carlo simulations in the literature, obtaining a very good agreement despite our approximations, thus validating our approach. As an additional result, we can show that, for future down-sized technologies that may be more sensitive to radiation effects, the contribution of neutrons in the 1–10 MeV energy range to the SER is expected to decrease.

A multivariate Gaussian mixture model for anomaly detection in transient current signature of control element drive mechanism

The analysis of transient coil current signals for anomaly detection in a control element drive mechanism (CEDM) involves identifying signature mismatch based on comparison to normal signal profiles. This paper presents an anomaly detection model for optimizing the health monitoring framework for CEDMs. Our approach is a machine learning clustering algorithm based on the multivariate Gaussian Mixture Model (GMM), for the detection of incipient abnormal transient signatures in CEDM coils. The underlying considerations for the choice of a GMM-based clustering approach are that (1) the transient coil current profiles at normal operational behavior are very similar, and (2) only very few data are usually available that describe anomalous CEDM behavior. Since available data is heavily skewed toward normal patterns, this methodology allows us to establish the norm for detecting anomalous patterns based on a preset detection threshold. The model evaluation result shows a very low false-negative rate for anomaly detection.

Characterization of the highly fractured zone at the Grimsel Test Site based on hydraulic tomography

Abstract. In this study, we infer the structural and hydraulic properties of the highly fractured zone at the Grimsel Test Site in Switzerland using a stochastic inversion method. The fractured rock is modeled directly as a discrete fracture network (DFN) within an impermeable rock matrix. Cross-hole transient pressure signals recorded from constant-rate injection tests at different intervals provide the basis for the (herein presented) first field application of the inversion. The experimental setup is realized by a multi-packer system. The geological mapping of the structures intercepted by boreholes as well as data from previous studies that were undertaken as part of the In Situ Stimulation and Circulation (ISC) experiments facilitate the setup of the site-dependent conceptual and forward model. The inversion results show that two preferential flow paths between the two boreholes can be distinguished: one is dominated by fractures with large hydraulic apertures, whereas the other path consists mainly of fractures with a smaller aperture. The probability of fractures linking both flow paths increases the closer we get to the second injection borehole. These results are in accordance with the findings of other studies conducted at the site during the ISC measurement campaign and add new insights into the highly fractured zone at this prominent study site.

Efficient Estimation of Empirical Green’s Function by Removing Transients From Sensor Data Using Time–Frequency Normalization Technique

Clean ambient noise data is the preliminary requirement to perform ambient noise tomography of the earth. Conventional 1-bit time-domain normalization (TDN) is the most popular method to remove the effect of earthquakes and other transient signals obscured by the continuous recording of ambient noise at the sensors. The presence of monochromatic noise sources is also reduced by performing spectral whitening. In this manner, recorded ambient noise data is first normalized in the time domain and then in the frequency domain. It is observed that these normalization procedures do not make the amplitude “even” within the small segments of the complete normalized ambient noise time series and hence, ambient noise remains nondiffusive within these segments. Therefore, in this article, a time–frequency normalization technique is proposed to efficiently remove the temporal and spectral components of the unwanted signals. In this article, continuous wavelet transform (CWT) is used to transform raw ambient noise time series to time–frequency domain to exploit its time–frequency localization property. Complex Morlet wavelet best captures the oscillations present in the raw ambient noise data and then upon normalizing CWT coefficients, a nearly diffusive ambient noise data is retrieved which is suitable to estimate empirical Green’s function (EGF). The performance of the proposed method is tested using synthetic data and the EGFs are estimated using real ambient noise data of a subset of STS-2 USArray sensors. The EGFs are evaluated quantitatively by calculating the signal-to-noise ratio (SNR) and comparing them with those estimated using the conventional method.

Methods of transient em data compression

A transient electromagnetic signal is recorded in an earth formation while drilling at multiple times. Data for subsequent interpretation is transmitted via a hydrochannel with a narrow bandwidth, for that reason the amount of transmitted information is limited. Therefore, data compression is an indispensable element of processing. It is important to determine what parameters and how many of them are necessary to describe the measured EMF curve. The idea is to transfer to the surface only significant parameters allowing to restore the recorded curve with a given accuracy. The paper proposes simple and fast data compression methods based on the spline approximation of the signal. Also, an orthogonal basis in the measurement space is used to describe each signal by a linear combination of vectors from this basis. We analyze transient curves and consider various magnetic field components. Numerical experiments show how many parameters are required to describe and reconstruct the signal of a deep-reading electromagnetic tool.

A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time

Early on-site diagnosis of mild traumatic brain injury (mTBI) will provide the best guidance for clinical practice. However, existing methods and sensors cannot provide sufficiently detailed physical information related to the blunt force impact. In the present work, a smart helmet with a single embedded fiber Bragg grating (FBG) sensor is developed, which can monitor complex blunt force impact events in real time under both wired and wireless modes. The transient oscillatory signal "fingerprint" can specifically reflect the impact-caused physical deformation of the local helmet structure. By combination with machine learning algorithms, the unknown transient impact can be recognized quickly and accurately in terms of impact magnitude, direction, and latitude. Optimization of the training dataset was also validated, and the boosted ML models, such as the S-SVM+ and S-IBK+, are able to predict accurately with complex databases. Thus, the ML-FBG smart helmet system developed by this work may become a crucial intervention alternative during a traumatic brain injury event.

A two-site triplet exciton hopping model: Application to 3P700.

A model is presented describing the effect on spin-polarized transient EPR signals caused by incoherent state hopping between two sites. It is shown that the size of the spin state space can be reduced by half to the subspace described by the site-average Hamiltonian and that the dynamics of the system results in a redistribution of the population between its eigenstates. Analytical expressions for the rates of population redistribution and the line shape are derived for the general case in which the back-and-forth rates are unequal. The EPR signals calculated using these expressions are in very good agreement with those obtained by direct numerical solution of the density matrix rate equations. The model is then used to investigate the influence of exciton hopping on triplet state transient EPR spectra. Using the triplet state of the primary donor of Photosystem I as an example, it is shown that the influence of unequal hopping rates becomes more pronounced in the spectrum at longer delay times after the laser flash.

Discriminating gas molecules at room temperature by UV light modulation (ULM) of nonselective metal oxide sensors

Though temperature modulation has demonstrated its capability in addressing selectivity issue of metal oxide semiconductor (MOS) gas sensors, integration of (micro)heater not only drastically adds power consumption and device manufacture complexity, but also hinders the use of flexible polymer substrate/platform. Exploring an approach enabling outstanding selectivity at room temperature (RT) is highly required, but it remains a big challenge. Herein, UV light modulation (ULM) with a rectangular UV wave has been proposed to regulate the transient response signals of sensor arrays, composed of ZnO/(Sr0.6Bi0.305)2Bi2O7 (ZnO/SBO), ZnO and In2O3. Upon pulsed UV excitation, chemisorption (or redox reaction) of injected target vapors (on MOS surface) could be activated at RT. Arising from the specific gas/MOS binding configurations for different gases and MOSs, quite different recovery kinetics under UV illumination (associated with deactivation of chemisorbed target molecules) were found, which endow nonselective MOS sensors with superior selectivity. Multilayer perceptron (MLP) indicates a 99% recognition accuracy ratio of eleven kinds of tested gases, including four kinds of benzene molecules. Our work demonstrates that transient response signals during ULM contain rich features of adsorbed gas molecules, which offers an opportunity for the facile design of advanced smart gas molecule recognition chips on both rigid and flexible platforms.

Magnetic Large‐Mesoporous Nanoreactors Enable Enzymatically Regulated Background‐Free and Persistently Signalling Diseases Diagnosis

AbstractDiverse diseases and increasing prevalence pose a serious threat to public health. Point‐of‐care testing (POCT) techniques have imposed superior requirements over sensitivity, selectivity, robustness, affordability, and high‐throughput. However, transient signal, complex sample pretreatment, and low signal‐to‐noise ratio make POCT severely limited in detection accuracy, efficiency, and sensitivity. Here, an enzyme‐assisted magnetic large‐mesoporous nanoreactor (FS) is constructed for achieving persistent‐chemiluminescence signal output and eliminating matrix interference in disease diagnosis. The core‐shell structured FS synthesized via an interface coassembly method exhibits uniform size, large and open mesopores (≈22 nm), and intrinsic magnetic separability. Such unique FS acts as efficient nanoreactor for confined cascade reactions enable efficient persistent‐chemiluminescence (pCL) signal transduction and high‐SNR chromogenic analysis of diverse biomarkers. The developed pCL assays facilitate high‐sensitive determination of chronic disease biomarkers glucose and uric acid with detection limits (DL) of 5.4 mg L−1 and 151.2 ng L−1, respectively. The proposed chromogenic immunoassay enables an ultrasensitive and visual determination of alpha‐fetoprotein with a DL of 1.2 ng L−1, which is superior to previously published immunoassays. The feasibility of the developed methods for real‐world applications are demonstrated in 159 clinical serum samples, and the determination results agree well with the clinical data. The proposed technique is expected to promote highly sensitive disease diagnosis in primary medical institutions and resource‐limited areas since not relying on expensive automatic sampling and testing instruments. The good flexibility of the customizable nanoreactor makes it a powerful tool for developing various POCT techniques for rapid, sensitive, and accurate diseases diagnosis.

ACT-26 PATTERN OF RECURRENCE IN NEWLY DIAGNOSED GLIOBLASTOMA TREATED WITH PHOTODYNAMIC THERAPY

Abstract In 2016, photodynamic therapy (PDT) system using talaporfin sodium was introduced to our hospital. By December 2021, we have experienced 22 cases (15 males, 7 females, 57.9±18.6 years old) of newly diagnosed glioblastoma (GBM) treated with PDT. The recurrence pattern of 22 cases was retrospectively examined. In August 2022, 7 cases are still alive. Laser irradiation was performed at an average of 7.6±3.4 sites. Tumor localization was solitary and superficial in 14 cases. Extent of tumor resection was 89.3±18.5%, and the postoperative KPS was 86.4±15.0%. Median PFS was 11 months, median OS was 18 months, and 5 patients survived for more than 3 years. Recurrences were observed in 16 cases, and the recurrence types were local (LR) in 9 cases, distant (DR) in 3 cases, and both in 4 cases. Two of the remaining 6 cases, which were judged to have no recurrence at present, had Gd-enhancing lesions locally and were judged to have LR. Therefore, re-resection was performed in both cases, but the pathological diagnosis was gliosis. Fujita et al. (J Neurooncol 155:81-92, 2021) reported that acute response of high-grade glioma to PDT can be detected as linear transient hyperintense signal (LTHS) on diffusion-weighted imaging (DWI), and that All of the LRs arose from areas that did not show LTHS on DWI obtained on day 1 after PDT. Furthermore, they reported that this characteristic finding is useful for monitoring LR after PDT. In our cases, 3 DRs and 4 existing long-term survivors (including 2 with local gliosis) showed nearly LTHS throughout the resected wall. On the other hand, partial absence of LTHS was confirmed in 13 cases containing LR. PDT provides effective local control, but it requires careful irradiation planning to avoid missing areas of irradiation, and LTHS on DWI could indicate whether effective irradiation has been achieved.